King_et_al_2022_revisions

Author

JM Varberg

Published

November 14, 2022

Load required packages

Code

library(tidyverse)
library(cowplot)
library(ggpubr)
library(ggforce)
library(data.table)
xfun::pkg_load2(c('htmltools', 'mime'))

Code

process <- function(df) {
    
    data <- df |>
        mutate(Time = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                str_detect(Image, "frame_2") ~ "Anaphase",
                                str_detect(Image, "frame_3") ~ "Post"),
               Time = factor(Time, levels=c("Pre", "Anaphase", "Post")),
               NE_Nuc_ratio = ((NE1_int + NE2_int)/2) / Nuc_int)
    return(data)
}

plots_sina <- function(df, label_n){
    df |>
        filter(Time != "Post") |> 
        ggplot(aes(x=Time, y=Nuc_Ratio)) +
        geom_sina(alpha=0.4, size=1.5) +
        stat_summary(geom="pointrange", fun.data=mean_sd, position="dodge") +
        stat_compare_means(label.x = 2, label.y = 0.1) +
        stat_compare_means(label = "p.signif") +
        annotate("text", x=0.75, y=0.1, label = label_n) +
        xlab("Time") +
        ylab("Nuc/NE Ratio (a.u.)") +
        theme_cowplot()
}

jmv_dataTable <- function(df) {
    df |>
        DT::datatable(extensions = 'Buttons', options = list(
        scrollY="true",
        scrollX="true",
        pageLength = 10,
        lengthMenu = c(10, 25, 50, 100), 
        dom = 'Blfrtip',
        buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
      )
    )
}

#apply min-max normalization
min_max_norm <- function(x, ...) {
    return((x - min(x, ...))/(max(x, ...) - min(x, ...)))
}

plots_traces <- function(df, hline) {
    df |> 
        ggplot(aes(x=X, y=Y, group=Image)) +
        geom_line(alpha=0.1) +
        stat_summary(aes(group=Time.point), fun=mean, geom="line", colour="black", size = 1) +
        geom_hline(yintercept = hline, color="red", linetype="dashed", size = 1) +
        facet_wrap(~Time.point) +
        xlab("Distance (microns)") +
        ylab("GFP-Intensity (a.u.)") +
        theme_cowplot()
}

Mitosis Line Scans

Goal is to look at detachment during anaphase of mitotic divisions to compare to meiosis data. However, the histone marker does not work well for mitosis because it is excluded from the nucleolar region, so the masking approach used for meiosis images isn’t working.

Approach is to use line profiles across the nucleus, put through python tools for peak calling and for calculation of the Nuc/NE ratio. Essentially, NE peaks are called or manually defined (if needed when more than two peaks are detected), and the NE intensity is calculated as the average of the two NE peak intensities. The nucleoplasmic intensity is calculated as the average intensity of the middle 50% of the region between the two NE peaks.

Images of the middle slices of nuclei were prepared by Grant, used for line profiles peformed by JMV in Fiji. A line width of 5 pixels and length of 5 microns was used, saving out CSV files for the X/Y coordinates of the resulting profile. These are read into the python workflow as input. Python notebook outputs a CSV for each image with the intensities and ratios calculated for each trace.

Code

#read in data for line profiles
nup2_mitosis <- fread("./data/NucRatios_Nup2-GFP_mitosis.csv")
pom34_mitosis <- fread("./data/NucRatios_Pom34-GFP_mitosis.csv")
nup60_mitosis <- fread("./data/NucRatios_Nup60-GFP_mitosis.csv")
nup1_mitosis <- fread("./data/NucRatios_Nup1-GFP_mitosis.csv")
mlp1_mitosis <- fread("./data/NucRatios_Mlp1-GFP_mitosis.csv")

nup2_mitosis_df <- process(nup2_mitosis)
nup2_mitosis_df$ID <- c("Nup2")
pom34_mitosis_df <- process(pom34_mitosis)
pom34_mitosis_df$ID <- c("Pom34")
nup60_mitosis_df <- process(nup60_mitosis)
nup60_mitosis_df$ID <- c("Nup60")
nup1_mitosis_df <- process(nup1_mitosis)
nup1_mitosis_df$ID <- c("Nup1")
mlp1_mitosis_df <- process(mlp1_mitosis)
mlp1_mitosis_df$ID <- c("Mlp1")

#my_comparisons <- list(c("Pre", "Anaphase"), c("Anaphase", "Post"), c("Pre", "Post"))

p1 <- nup2_mitosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 32") + 
    ylim(0,0.75) +
    ggtitle("Nup2-GFP, mitosis linescans")
ggsave2(plot = p1, "./plots/Nup2-GFP_mitosis_NucNE_Ratios.png")

p2 <- pom34_mitosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 22") +
    ylim(0,0.75) +
    ggtitle("Pom34-GFP, mitosis line scans")
ggsave2(plot=p2, "./plots/Pom34-GFP_mitosis_NucNE_Ratios.png")

p3 <- nup60_mitosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 34") +
    ylim(0,0.75) +
    ggtitle("Nup60-GFP, mitosis line scans")
ggsave2(plot=p3, "./plots/Nup60-GFP_mitosis_NucNE_Ratios.png")

p4 <- nup1_mitosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 42") +
    ylim(0,0.75) +
    ggtitle("Nup1-GFP, mitosis line scans")
ggsave2(plot=p4, "./plots/Nup1-GFP_mitosis_NucNE_Ratios.png")

p5 <- mlp1_mitosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 16") +
    ylim(0, 0.75) +
    ggtitle("Mlp1-GFP, mitosis line scans")
ggsave2(plot=p5, "./plots/Mlp1-GFP_mitosis_NucNE_Ratios.png")

Code

p <- plot_grid(p1, p2, p3, p4, p5, ncol=2, nrow=3)
p

Code

ggsave2("./plots/Combined_Nups_mitosis_Nuc_NE_ratios_plots.png", width=10, height=10, dpi=300)

#combine for summary stats and statistical tests
comb <- rbind(nup2_mitosis_df, pom34_mitosis_df, nup60_mitosis_df, nup1_mitosis_df, mlp1_mitosis_df)

Summary Stats for Mitosis Linescans

Code

comb |> 
    group_by(ID, Time) |> 
    get_summary_stats(type="common") |> 
    jmv_dataTable()

Wilcoxon Test Results, Mitosis

Code

comb |> 
    filter(Time != "Post") |> 
    group_by(ID) |> 
    rstatix::wilcox_test(Nuc_Ratio ~ Time) |> 
    jmv_dataTable()

Mitosis line scan plots

Alternative way of plotting the data, instead of showing the ratios as bar/sina plots, show the individual profiles along with the averaged profile for each group. This is similar to the pombe figures in Fig. 10.

Code

pom34_profile_files <- list.files("./Middle_slices_Pom34_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
pom34_profiles <- lapply(pom34_profile_files, fread)
names(pom34_profiles) <- pom34_profile_files


pom34_profiles <- lapply(pom34_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})

pom34_trace_df <- bind_rows(pom34_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))


p1 <- pom34_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.25) +
    ggtitle("Pom34-GFP mitosis line scans")
p1

Code

###Nup2

nup2_profile_files <- list.files("./Middle_slices_Nup2_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
nup2_profiles <- lapply(nup2_profile_files, fread)
names(nup2_profiles) <- nup2_profile_files
nup2_profiles <- lapply(nup2_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})
nup2_trace_df <- bind_rows(nup2_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p2 <- nup2_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.25) + 
    ggtitle("Nup2-GFP, mitosis line scans")
p2

Code

###Nup60

nup60_profile_files <- list.files("./Middle_slices_Nup60_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
nup60_profiles <- lapply(nup60_profile_files, fread)
names(nup60_profiles) <- nup60_profile_files
nup60_profiles <- lapply(nup60_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})
nup60_trace_df <- bind_rows(nup60_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p3 <- nup60_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.25) + 
    ggtitle("Nup60-GFP, mitosis line scans")
p3

Code

###Nup1

nup1_profile_files <- list.files("./Middle_slices_Nup1_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
nup1_profiles <- lapply(nup1_profile_files, fread)
names(nup1_profiles) <- nup1_profile_files
nup1_profiles <- lapply(nup1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})
nup1_trace_df <- bind_rows(nup1_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p4 <- nup1_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.25) + 
    ggtitle("Nup1-GFP, mitosis line scans")
p4

Code

###Mlp1

mlp1_profile_files <- list.files("./Middle_slices_Mlp1_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
mlp1_profiles <- lapply(mlp1_profile_files, fread)
names(mlp1_profiles) <- mlp1_profile_files
mlp1_profiles <- lapply(mlp1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})
mlp1_trace_df <- bind_rows(mlp1_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p5 <- mlp1_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.25) + 
    ggtitle("Mlp1-GFP, mitosis line scans")
p5

Code

plot_grid(p1, p2, p3, p4, p5, ncol=2, nrow=3)

Code

ggsave2("./plots/Combined_Nups_mitosis_plot_profiles_fig10_style.png")

Meiosis Line Scans

Since we’d like to make a comment on the relative amount of detachment between meiosis vs. mitosis, we want to compare ratios derived using the same analysis method. To do this, we will re-analyze the meiosis images using the line scan approach so we can directly compare to line scan values from mitotic data.

Code

pom34_meiosis <- fread("./data/NucRatios_Pom34-GFP_meiosis.csv")
nup2_meiosis <- fread("./data/NucRatios_Nup2-GFP_meiosis.csv")
nup1_meiosis <- fread("./data/NucRatios_Nup1-GFP_meiosis.csv")
nup60_meiosis <- fread("./data/NucRatios_Nup60-GFP_meiosis.csv")
mlp1_meiosis <- fread("./data/NucRatios_Mlp1-GFP_meiosis.csv")

pom34_meiosis_df <- process(pom34_meiosis)
pom34_meiosis_df$ID <- c("Pom34")
nup2_meiosis_df <- process(nup2_meiosis)
nup2_meiosis_df$ID <- c("Nup2")
nup1_meiosis_df <- process(nup1_meiosis)
nup1_meiosis_df$ID <- c("Nup1")
nup60_meiosis_df <- process(nup60_meiosis)
nup60_meiosis_df$ID <- c("Nup60")
mlp1_meiosis_df <- process(mlp1_meiosis)
mlp1_meiosis_df$ID <- c("Mlp1")

Meiosis Summary Stats

Code

combdf <- rbind(pom34_meiosis_df, nup2_meiosis_df, nup1_meiosis_df, nup60_meiosis_df, mlp1_meiosis_df)

combdf |> 
    group_by(Time, ID) |> 
    get_summary_stats() |> 
    jmv_dataTable()

Meiosis line scan plots

Code

p <- pom34_meiosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 54") + 
    ylim(0, 1.5) + 
    ggtitle("Pom34-GFP, meiosis linescans") 
p

Code

ggsave2(plot=p, "./plots/Pom34-GFP_meiosis_NucNE_Ratios.png")

Code

p2 <- nup2_meiosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 56") +
    ylim(0, 1.5) + 
    ggtitle("Nup2-GFP, meiosis line scans")
p2

Code

ggsave2(plot=p2, "./plots/Nup2-GFP_meiosis_NucNE_Ratios.png")

Code

p3 <- nup1_meiosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 26") +
    ylim(0, 1.5) + 
    ggtitle("Nup1-GFP, meiosis line scans")
p3

Code

ggsave2(plot=p3, "./plots/Nup1-GFP_meiosis_NucNE_Ratios.png")

Code

p4 <- nup60_meiosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 30") +
    ylim(0, 1.5) + 
    ggtitle("Nup60-GFP, meiosis line scans")
p4

Code

ggsave2(plot=p4, "./plots/Nup60-GFP_meiosis_NucNE_Ratios.png")

Code

p5 <- mlp1_meiosis_df |> 
    filter(Time != "Post") |> 
    plots_sina(label_n = "n = 23") +
    ylim(0, 1.5) + 
    ggtitle("Mlp1-GFP, meiosis line scans")
p5

Code

ggsave2(plot=p5, "./plots/Mlp1-GFP_meiosis_NucNE_Ratios.png")

Code

plot_grid(p, p2, p3, p4, p5, ncol=2, nrow=3)

Meiosis line scan plots

Code

#pom34 meiosis line traces
pom34_profile_files <- list.files("./Middle_slices_Pom34_meosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
pom34_profiles <- lapply(pom34_profile_files, fread)
names(pom34_profiles) <- pom34_profile_files

pom34_profiles <- lapply(pom34_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})

pom34_trace_df <- bind_rows(pom34_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p1 <- pom34_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.20) + 
    ggtitle("Pom34-GFP, meiosis")
p1

Code

ggsave2("./plots/Pom34-GFP_meiosis_line_traces.png", width=7, height=5, units="in", dpi=300)

Code

#nup2 meiosis line traces
Nup2_profile_files <- list.files("./Middle_slices_Nup2_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
Nup2_profiles <- lapply(Nup2_profile_files, fread)
names(Nup2_profiles) <- Nup2_profile_files

Nup2_profiles <- lapply(Nup2_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})

Nup2_trace_df <- bind_rows(Nup2_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p2 <- Nup2_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.20) + 
    ggtitle("Nup2-GFP, meiosis")
p2

Code

#nup1 meiosis line traces
Nup1_profile_files <- list.files("./Middle_slices_Nup1_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
Nup1_profiles <- lapply(Nup1_profile_files, fread)
names(Nup1_profiles) <- Nup1_profile_files

Nup1_profiles <- lapply(Nup1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})

Nup1_trace_df <- bind_rows(Nup1_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p3 <- Nup1_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.20) + 
    ggtitle("Nup1-GFP, meiosis")
p3

Code

#nup60 meiosis line traces
Nup60_profile_files <- list.files("./Middle_slices_Nup60_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
Nup60_profiles <- lapply(Nup60_profile_files, fread)
names(Nup60_profiles) <- Nup60_profile_files

Nup60_profiles <- lapply(Nup60_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})

Nup60_trace_df <- bind_rows(Nup60_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p4 <- Nup60_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.20) + 
    ggtitle("Nup60-GFP, meiosis")
p4

Code

#mlp1 meiosis line traces
Mlp1_profile_files <- list.files("./Middle_slices_Mlp1_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE)
Mlp1_profiles <- lapply(Mlp1_profile_files, fread)
names(Mlp1_profiles) <- Mlp1_profile_files

Mlp1_profiles <- lapply(Mlp1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))})

Mlp1_trace_df <- bind_rows(Mlp1_profiles, .id = "Image") |> 
    select(-V1) |> 
    mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre",
                                  str_detect(Image, "frame_2") ~ "Anaphase",
                                  str_detect(Image, "frame_3") ~ "Post"),
           Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post")))

p5 <- Mlp1_trace_df |> 
    filter(Time.point != "Post") |> 
    plots_traces(hline=0.20) + 
    ggtitle("Mlp1-GFP, meiosis")
p5

Code

plot_grid(p1, p2, p3, p4, p5, ncol=2, nrow=3)

Code

ggsave2("./plots/Combined_meiosis_line_traces.png", width=10, height=10, units="in", dpi=300)

Final figure

Want to make a final version of the figure comparing Pom34, Nup1, Nup60, Nup2, Mlp1 Pre vs. Anaphase based on line scan ratios for mitosis and meiosis. Normalize each Nup to its own “Pre” mean ratio. Plot should be styled similarly to Fig 1C from bioRxiv submission.

Code

#combined mitosis df
comb_mitosis <- comb |> mutate(Experiment = "Mitosis")

#combined meiosis df
comb_meiosis <- combdf |> mutate(Experiment = "Meiosis")

#join them together
full_df <- bind_rows(comb_mitosis, comb_meiosis)

#calculate mean values for pre for each nup

full_df_pre_means <- full_df |> 
    filter(Time == "Pre") %>% 
    group_by(Experiment, ID, Time) |> 
    summarise_at(vars(Nuc_Ratio), mean) |> 
    rename(PreAvg = Nuc_Ratio) |> 
    select(-Time)

#left join in the pre value for each nup and normalize with mutate

full_df_norm <- left_join(full_df, full_df_pre_means)

full_df_norm <- full_df_norm |> 
    mutate(Norm.Nuc.Ratio = Nuc_Ratio/PreAvg,
           ID = factor(ID, levels=c("Pom34", "Mlp1", "Nup1", "Nup2", "Nup60")),
           Experiment = factor(Experiment, levels = c("Mitosis", "Meiosis")))

#plot

full_df_norm |> 
    filter(Time != "Post") |>
    ggplot(aes(x=forcats::fct_rev(ID), y=Norm.Nuc.Ratio, color=Experiment)) +
    #geom_hline(yintercept = 1) +
    geom_sina(alpha=0.3, size=1, pch=16) +
    stat_summary(geom="pointrange", fun.data=mean_sd, position=position_dodge(width=0.9)) +
    #stat_compare_means(label="p.signif", ref.group="Pom34") +
    xlab("Normalized detachment index (a.u.)") +
    ylab("") +
    #scale_color_brewer(palette = "Paired") +
    scale_color_manual(values = c("darkslategrey", "darkslateblue")) +
    facet_wrap(~Experiment+Time, ncol=2) +
    theme_cowplot() +
    coord_flip() +
    theme(legend.position = "bottom") +
    theme(strip.text.x = element_text(size = 12, color = "black", face = "bold"),
          strip.background = NULL)

Code

ggsave2("./plots/Combined_Meiosis_Mitosis_Linescan_Ratios_color_3_legend.pdf", width=4, height=4.8, units="in")
xfun::embed_file("./plots/Combined_Meiosis_Mitosis_Linescan_Ratios_color_3_legend.pdf")

Download Combined_Meiosis_Mitosis_Linescan_Ratios_color_3_legend.pdf

Stats tests vs. Pom34 for linescans

These are the stats that are reported for Figure S2F in Table S5.

Summary Stats, Nuc Ratios

Code

full_df_norm |> 
    filter(Time != "Post") |> 
    group_by(Experiment, ID, Time) |> 
    rstatix::get_summary_stats(Norm.Nuc.Ratio, type="common") |> 
    jmv_dataTable()

Shapiro Test

Code

full_df_norm |> 
    filter(Time != "Post") |> 
    group_by(Experiment, ID, Time) |> 
    rstatix::shapiro_test(Norm.Nuc.Ratio) |> 
    jmv_dataTable()

Kruskal-Wallis (Non-parametric ANOVA)

Code

full_df_norm |> 
    filter(Time != "Post") |> 
    group_by(Experiment, Time) |>
    rstatix::kruskal_test(Norm.Nuc.Ratio ~ ID) |> 
    jmv_dataTable()

Dunns Post-hoc Test vs. Pom34

Code

#Dunn's post-hoc pairwise multiple comparisons
dunn <- full_df_norm %>% 
  group_by(Experiment, Time) %>% 
  rstatix::dunn_test(Norm.Nuc.Ratio ~ ID, p.adjust.method = "holm")

#'The dunn_test function performs all pair-wise comparisons, and so the adjusted p.value uses all tests for this correction. 
#'Since we only want to ask if any are different than Pom34-GFP, we subset those comparisons, 
#'then re-calculate adjusted p-values using the number of tests actually done.

dunn |> 
    filter(grepl("Pom34", group1), Time != "Post") |> 
    group_by(Experiment, Time) |>  
    mutate(Corr.p.adjust = p.adjust(p, method="holm"),
           Corr.p.signif = case_when(Corr.p.adjust <= 0.0001 ~ "****",
                                     Corr.p.adjust <= 0.001 ~ "***",
                                     Corr.p.adjust <= 0.01 ~ "**",
                                     Corr.p.adjust <= 0.05 ~ "*",
                                     TRUE ~ "ns")) |> 
    select(-p.adj, -p.adj.signif) |> 
    jmv_dataTable()

CSV files from Python Notebooks

These embedded CSVs are the output generated by the Python notebook for peak calling and intensity measurements from line scans.

Mitosis Line Scan Outputs

Code

xfun::embed_file("./data/NucRatios_Mlp1-GFP_mitosis.csv")

Download NucRatios_Mlp1-GFP_mitosis.csv

Code

xfun::embed_file("./data/NucRatios_Nup1-GFP_mitosis.csv")

Download NucRatios_Nup1-GFP_mitosis.csv

Code

xfun::embed_file("./data/NucRatios_Nup2-GFP_mitosis.csv")

Download NucRatios_Nup2-GFP_mitosis.csv

Code

xfun::embed_file("./data/NucRatios_Pom34-GFP_mitosis.csv")

Download NucRatios_Pom34-GFP_mitosis.csv

Code

xfun::embed_file("./data/NucRatios_Nup60-GFP_mitosis.csv")

Download NucRatios_Nup60-GFP_mitosis.csv

Meiosis Line Scan Outputs

Code

xfun::embed_file("./data/NucRatios_Nup2-GFP_meiosis.csv")

Download NucRatios_Nup2-GFP_meiosis.csv

Code

xfun::embed_file("./data/NucRatios_Pom34-GFP_meiosis.csv")

Download NucRatios_Pom34-GFP_meiosis.csv

Code

xfun::embed_file("./data/NucRatios_Nup1-GFP_meiosis.csv")

Download NucRatios_Nup1-GFP_meiosis.csv

Code

xfun::embed_file("./data/NucRatios_Nup60-GFP_meiosis.csv")

Download NucRatios_Nup60-GFP_meiosis.csv

Code

xfun::embed_file("./data/NucRatios_Mlp1-GFP_meiosis.csv")

Download NucRatios_Mlp1-GFP_meiosis.csv

--- title: "King_et_al_2022_revisions" author: "JM Varberg" date: '`r format(Sys.Date(), "%B %d, %Y")`' format: html: toc: true toc-depth: 4 toc-location: left link-external-icon: true link-external-newwindo: true code-fold: true code-overflow: scroll code-tools: true highlight-style: github code-copy: hover code-link: true theme: light: flatly dark: darkly comments: hypothesis: theme: clean execute: echo: true output: true error: false message: false warning: false fig-width: 8 fig-height: 8 self-contained: true --- ## Load required packages ```{r} library(tidyverse) library(cowplot) library(ggpubr) library(ggforce) library(data.table) xfun::pkg_load2(c('htmltools', 'mime')) ``` ```{r Custom Functions} process <- function(df) { data <- df |> mutate(Time = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time = factor(Time, levels=c("Pre", "Anaphase", "Post")), NE_Nuc_ratio = ((NE1_int + NE2_int)/2) / Nuc_int) return(data) } plots_sina <- function(df, label_n){ df |> filter(Time != "Post") |> ggplot(aes(x=Time, y=Nuc_Ratio)) + geom_sina(alpha=0.4, size=1.5) + stat_summary(geom="pointrange", fun.data=mean_sd, position="dodge") + stat_compare_means(label.x = 2, label.y = 0.1) + stat_compare_means(label = "p.signif") + annotate("text", x=0.75, y=0.1, label = label_n) + xlab("Time") + ylab("Nuc/NE Ratio (a.u.)") + theme_cowplot() } jmv_dataTable <- function(df) { df |> DT::datatable(extensions = 'Buttons', options = list( scrollY="true", scrollX="true", pageLength = 10, lengthMenu = c(10, 25, 50, 100), dom = 'Blfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print') ) ) } #apply min-max normalization min_max_norm <- function(x, ...) { return((x - min(x, ...))/(max(x, ...) - min(x, ...))) } plots_traces <- function(df, hline) { df |> ggplot(aes(x=X, y=Y, group=Image)) + geom_line(alpha=0.1) + stat_summary(aes(group=Time.point), fun=mean, geom="line", colour="black", size = 1) + geom_hline(yintercept = hline, color="red", linetype="dashed", size = 1) + facet_wrap(~Time.point) + xlab("Distance (microns)") + ylab("GFP-Intensity (a.u.)") + theme_cowplot() } ``` ## Mitosis Line Scans Goal is to look at detachment during anaphase of mitotic divisions to compare to meiosis data. However, the histone marker does not work well for mitosis because it is excluded from the nucleolar region, so the masking approach used for meiosis images isn't working. Approach is to use line profiles across the nucleus, put through python tools for peak calling and for calculation of the Nuc/NE ratio. Essentially, NE peaks are called or manually defined (if needed when more than two peaks are detected), and the NE intensity is calculated as the average of the two NE peak intensities. The nucleoplasmic intensity is calculated as the average intensity of the middle 50% of the region between the two NE peaks. Images of the middle slices of nuclei were prepared by Grant, used for line profiles peformed by JMV in Fiji. A line width of 5 pixels and length of 5 microns was used, saving out CSV files for the X/Y coordinates of the resulting profile. These are read into the python workflow as input. Python notebook outputs a CSV for each image with the intensities and ratios calculated for each trace. ```{r} #| fig-height: 5 #read in data for line profiles nup2_mitosis <- fread("./data/NucRatios_Nup2-GFP_mitosis.csv") pom34_mitosis <- fread("./data/NucRatios_Pom34-GFP_mitosis.csv") nup60_mitosis <- fread("./data/NucRatios_Nup60-GFP_mitosis.csv") nup1_mitosis <- fread("./data/NucRatios_Nup1-GFP_mitosis.csv") mlp1_mitosis <- fread("./data/NucRatios_Mlp1-GFP_mitosis.csv") nup2_mitosis_df <- process(nup2_mitosis) nup2_mitosis_df$ID <- c("Nup2") pom34_mitosis_df <- process(pom34_mitosis) pom34_mitosis_df$ID <- c("Pom34") nup60_mitosis_df <- process(nup60_mitosis) nup60_mitosis_df$ID <- c("Nup60") nup1_mitosis_df <- process(nup1_mitosis) nup1_mitosis_df$ID <- c("Nup1") mlp1_mitosis_df <- process(mlp1_mitosis) mlp1_mitosis_df$ID <- c("Mlp1") #my_comparisons <- list(c("Pre", "Anaphase"), c("Anaphase", "Post"), c("Pre", "Post")) p1 <- nup2_mitosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 32") + ylim(0,0.75) + ggtitle("Nup2-GFP, mitosis linescans") ggsave2(plot = p1, "./plots/Nup2-GFP_mitosis_NucNE_Ratios.png") p2 <- pom34_mitosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 22") + ylim(0,0.75) + ggtitle("Pom34-GFP, mitosis line scans") ggsave2(plot=p2, "./plots/Pom34-GFP_mitosis_NucNE_Ratios.png") p3 <- nup60_mitosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 34") + ylim(0,0.75) + ggtitle("Nup60-GFP, mitosis line scans") ggsave2(plot=p3, "./plots/Nup60-GFP_mitosis_NucNE_Ratios.png") p4 <- nup1_mitosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 42") + ylim(0,0.75) + ggtitle("Nup1-GFP, mitosis line scans") ggsave2(plot=p4, "./plots/Nup1-GFP_mitosis_NucNE_Ratios.png") p5 <- mlp1_mitosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 16") + ylim(0, 0.75) + ggtitle("Mlp1-GFP, mitosis line scans") ggsave2(plot=p5, "./plots/Mlp1-GFP_mitosis_NucNE_Ratios.png") ``` ```{r} #| fig-width: 12 #| fig-height: 12 p <- plot_grid(p1, p2, p3, p4, p5, ncol=2, nrow=3) p ggsave2("./plots/Combined_Nups_mitosis_Nuc_NE_ratios_plots.png", width=10, height=10, dpi=300) #combine for summary stats and statistical tests comb <- rbind(nup2_mitosis_df, pom34_mitosis_df, nup60_mitosis_df, nup1_mitosis_df, mlp1_mitosis_df) ``` #### Summary Stats for Mitosis Linescans ```{r} comb |> group_by(ID, Time) |> get_summary_stats(type="common") |> jmv_dataTable() ``` #### Wilcoxon Test Results, Mitosis ```{r} comb |> filter(Time != "Post") |> group_by(ID) |> rstatix::wilcox_test(Nuc_Ratio ~ Time) |> jmv_dataTable() ``` ### Mitosis line scan plots Alternative way of plotting the data, instead of showing the ratios as bar/sina plots, show the individual profiles along with the averaged profile for each group. This is similar to the pombe figures in Fig. 10. ```{r} #| fig-height: 5 pom34_profile_files <- list.files("./Middle_slices_Pom34_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) pom34_profiles <- lapply(pom34_profile_files, fread) names(pom34_profiles) <- pom34_profile_files pom34_profiles <- lapply(pom34_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) pom34_trace_df <- bind_rows(pom34_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p1 <- pom34_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.25) + ggtitle("Pom34-GFP mitosis line scans") p1 ###Nup2 nup2_profile_files <- list.files("./Middle_slices_Nup2_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) nup2_profiles <- lapply(nup2_profile_files, fread) names(nup2_profiles) <- nup2_profile_files nup2_profiles <- lapply(nup2_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) nup2_trace_df <- bind_rows(nup2_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p2 <- nup2_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.25) + ggtitle("Nup2-GFP, mitosis line scans") p2 ###Nup60 nup60_profile_files <- list.files("./Middle_slices_Nup60_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) nup60_profiles <- lapply(nup60_profile_files, fread) names(nup60_profiles) <- nup60_profile_files nup60_profiles <- lapply(nup60_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) nup60_trace_df <- bind_rows(nup60_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p3 <- nup60_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.25) + ggtitle("Nup60-GFP, mitosis line scans") p3 ###Nup1 nup1_profile_files <- list.files("./Middle_slices_Nup1_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) nup1_profiles <- lapply(nup1_profile_files, fread) names(nup1_profiles) <- nup1_profile_files nup1_profiles <- lapply(nup1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) nup1_trace_df <- bind_rows(nup1_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p4 <- nup1_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.25) + ggtitle("Nup1-GFP, mitosis line scans") p4 ###Mlp1 mlp1_profile_files <- list.files("./Middle_slices_Mlp1_mitosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) mlp1_profiles <- lapply(mlp1_profile_files, fread) names(mlp1_profiles) <- mlp1_profile_files mlp1_profiles <- lapply(mlp1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) mlp1_trace_df <- bind_rows(mlp1_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p5 <- mlp1_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.25) + ggtitle("Mlp1-GFP, mitosis line scans") p5 ``` ```{r} plot_grid(p1, p2, p3, p4, p5, ncol=2, nrow=3) ggsave2("./plots/Combined_Nups_mitosis_plot_profiles_fig10_style.png") ``` ## Meiosis Line Scans Since we'd like to make a comment on the relative amount of detachment between meiosis vs. mitosis, we want to compare ratios derived using the same analysis method. To do this, we will re-analyze the meiosis images using the line scan approach so we can directly compare to line scan values from mitotic data. ```{r} pom34_meiosis <- fread("./data/NucRatios_Pom34-GFP_meiosis.csv") nup2_meiosis <- fread("./data/NucRatios_Nup2-GFP_meiosis.csv") nup1_meiosis <- fread("./data/NucRatios_Nup1-GFP_meiosis.csv") nup60_meiosis <- fread("./data/NucRatios_Nup60-GFP_meiosis.csv") mlp1_meiosis <- fread("./data/NucRatios_Mlp1-GFP_meiosis.csv") pom34_meiosis_df <- process(pom34_meiosis) pom34_meiosis_df$ID <- c("Pom34") nup2_meiosis_df <- process(nup2_meiosis) nup2_meiosis_df$ID <- c("Nup2") nup1_meiosis_df <- process(nup1_meiosis) nup1_meiosis_df$ID <- c("Nup1") nup60_meiosis_df <- process(nup60_meiosis) nup60_meiosis_df$ID <- c("Nup60") mlp1_meiosis_df <- process(mlp1_meiosis) mlp1_meiosis_df$ID <- c("Mlp1") ``` #### Meiosis Summary Stats ```{r} combdf <- rbind(pom34_meiosis_df, nup2_meiosis_df, nup1_meiosis_df, nup60_meiosis_df, mlp1_meiosis_df) combdf |> group_by(Time, ID) |> get_summary_stats() |> jmv_dataTable() ``` ### Meiosis line scan plots ```{r} p <- pom34_meiosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 54") + ylim(0, 1.5) + ggtitle("Pom34-GFP, meiosis linescans") p ggsave2(plot=p, "./plots/Pom34-GFP_meiosis_NucNE_Ratios.png") ``` ```{r} p2 <- nup2_meiosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 56") + ylim(0, 1.5) + ggtitle("Nup2-GFP, meiosis line scans") p2 ggsave2(plot=p2, "./plots/Nup2-GFP_meiosis_NucNE_Ratios.png") ``` ```{r} p3 <- nup1_meiosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 26") + ylim(0, 1.5) + ggtitle("Nup1-GFP, meiosis line scans") p3 ggsave2(plot=p3, "./plots/Nup1-GFP_meiosis_NucNE_Ratios.png") ``` ```{r} p4 <- nup60_meiosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 30") + ylim(0, 1.5) + ggtitle("Nup60-GFP, meiosis line scans") p4 ggsave2(plot=p4, "./plots/Nup60-GFP_meiosis_NucNE_Ratios.png") ``` ```{r} p5 <- mlp1_meiosis_df |> filter(Time != "Post") |> plots_sina(label_n = "n = 23") + ylim(0, 1.5) + ggtitle("Mlp1-GFP, meiosis line scans") p5 ggsave2(plot=p5, "./plots/Mlp1-GFP_meiosis_NucNE_Ratios.png") ``` ```{r} #| fig-width: 10 #| fig-height: 10 plot_grid(p, p2, p3, p4, p5, ncol=2, nrow=3) ``` ### Meiosis line scan plots ```{r} #| fig-height: 5 #pom34 meiosis line traces pom34_profile_files <- list.files("./Middle_slices_Pom34_meosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) pom34_profiles <- lapply(pom34_profile_files, fread) names(pom34_profiles) <- pom34_profile_files pom34_profiles <- lapply(pom34_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) pom34_trace_df <- bind_rows(pom34_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p1 <- pom34_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.20) + ggtitle("Pom34-GFP, meiosis") p1 ggsave2("./plots/Pom34-GFP_meiosis_line_traces.png", width=7, height=5, units="in", dpi=300) ``` ```{r} #| fig-height: 5 #nup2 meiosis line traces Nup2_profile_files <- list.files("./Middle_slices_Nup2_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) Nup2_profiles <- lapply(Nup2_profile_files, fread) names(Nup2_profiles) <- Nup2_profile_files Nup2_profiles <- lapply(Nup2_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) Nup2_trace_df <- bind_rows(Nup2_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p2 <- Nup2_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.20) + ggtitle("Nup2-GFP, meiosis") p2 ``` ```{r} #| fig-height: 5 #nup1 meiosis line traces Nup1_profile_files <- list.files("./Middle_slices_Nup1_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) Nup1_profiles <- lapply(Nup1_profile_files, fread) names(Nup1_profiles) <- Nup1_profile_files Nup1_profiles <- lapply(Nup1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) Nup1_trace_df <- bind_rows(Nup1_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p3 <- Nup1_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.20) + ggtitle("Nup1-GFP, meiosis") p3 ``` ```{r} #| fig-height: 5 #nup60 meiosis line traces Nup60_profile_files <- list.files("./Middle_slices_Nup60_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) Nup60_profiles <- lapply(Nup60_profile_files, fread) names(Nup60_profiles) <- Nup60_profile_files Nup60_profiles <- lapply(Nup60_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) Nup60_trace_df <- bind_rows(Nup60_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p4 <- Nup60_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.20) + ggtitle("Nup60-GFP, meiosis") p4 ``` ```{r} #| fig-height: 5 #mlp1 meiosis line traces Mlp1_profile_files <- list.files("./Middle_slices_Mlp1_meiosis/line_profiles/Plot_ProfileCSVs/", pattern = ".csv", full.names = TRUE) Mlp1_profiles <- lapply(Mlp1_profile_files, fread) names(Mlp1_profiles) <- Mlp1_profile_files Mlp1_profiles <- lapply(Mlp1_profiles, function(df) { df <- df |> mutate(Y = min_max_norm(Y, na.rm=TRUE))}) Mlp1_trace_df <- bind_rows(Mlp1_profiles, .id = "Image") |> select(-V1) |> mutate(Time.point = case_when(str_detect(Image, "frame_1") ~ "Pre", str_detect(Image, "frame_2") ~ "Anaphase", str_detect(Image, "frame_3") ~ "Post"), Time.point = factor(Time.point, levels=c("Pre", "Anaphase", "Post"))) p5 <- Mlp1_trace_df |> filter(Time.point != "Post") |> plots_traces(hline=0.20) + ggtitle("Mlp1-GFP, meiosis") p5 ``` ```{r} plot_grid(p1, p2, p3, p4, p5, ncol=2, nrow=3) ggsave2("./plots/Combined_meiosis_line_traces.png", width=10, height=10, units="in", dpi=300) ``` ## Final figure Want to make a final version of the figure comparing Pom34, Nup1, Nup60, Nup2, Mlp1 Pre vs. Anaphase based on line scan ratios for mitosis and meiosis. Normalize each Nup to its own "Pre" mean ratio. Plot should be styled similarly to Fig 1C from bioRxiv submission. ```{r} #| fig-width: 4 #| fig-height: 8 #combined mitosis df comb_mitosis <- comb |> mutate(Experiment = "Mitosis") #combined meiosis df comb_meiosis <- combdf |> mutate(Experiment = "Meiosis") #join them together full_df <- bind_rows(comb_mitosis, comb_meiosis) #calculate mean values for pre for each nup full_df_pre_means <- full_df |> filter(Time == "Pre") %>% group_by(Experiment, ID, Time) |> summarise_at(vars(Nuc_Ratio), mean) |> rename(PreAvg = Nuc_Ratio) |> select(-Time) #left join in the pre value for each nup and normalize with mutate full_df_norm <- left_join(full_df, full_df_pre_means) full_df_norm <- full_df_norm |> mutate(Norm.Nuc.Ratio = Nuc_Ratio/PreAvg, ID = factor(ID, levels=c("Pom34", "Mlp1", "Nup1", "Nup2", "Nup60")), Experiment = factor(Experiment, levels = c("Mitosis", "Meiosis"))) #plot full_df_norm |> filter(Time != "Post") |> ggplot(aes(x=forcats::fct_rev(ID), y=Norm.Nuc.Ratio, color=Experiment)) + #geom_hline(yintercept = 1) + geom_sina(alpha=0.3, size=1, pch=16) + stat_summary(geom="pointrange", fun.data=mean_sd, position=position_dodge(width=0.9)) + #stat_compare_means(label="p.signif", ref.group="Pom34") + xlab("Normalized detachment index (a.u.)") + ylab("") + #scale_color_brewer(palette = "Paired") + scale_color_manual(values = c("darkslategrey", "darkslateblue")) + facet_wrap(~Experiment+Time, ncol=2) + theme_cowplot() + coord_flip() + theme(legend.position = "bottom") + theme(strip.text.x = element_text(size = 12, color = "black", face = "bold"), strip.background = NULL) ggsave2("./plots/Combined_Meiosis_Mitosis_Linescan_Ratios_color_3_legend.pdf", width=4, height=4.8, units="in") xfun::embed_file("./plots/Combined_Meiosis_Mitosis_Linescan_Ratios_color_3_legend.pdf") ``` ## Stats tests vs. Pom34 for linescans These are the stats that are reported for Figure S2F in Table S5. ### Summary Stats, Nuc Ratios ```{r} full_df_norm |> filter(Time != "Post") |> group_by(Experiment, ID, Time) |> rstatix::get_summary_stats(Norm.Nuc.Ratio, type="common") |> jmv_dataTable() ``` ### Shapiro Test ```{r} full_df_norm |> filter(Time != "Post") |> group_by(Experiment, ID, Time) |> rstatix::shapiro_test(Norm.Nuc.Ratio) |> jmv_dataTable() ``` ### Kruskal-Wallis (Non-parametric ANOVA) ```{r} full_df_norm |> filter(Time != "Post") |> group_by(Experiment, Time) |> rstatix::kruskal_test(Norm.Nuc.Ratio ~ ID) |> jmv_dataTable() ``` ### Dunns Post-hoc Test vs. Pom34 ```{r} #Dunn's post-hoc pairwise multiple comparisons dunn <- full_df_norm %>% group_by(Experiment, Time) %>% rstatix::dunn_test(Norm.Nuc.Ratio ~ ID, p.adjust.method = "holm") #'The dunn_test function performs all pair-wise comparisons, and so the adjusted p.value uses all tests for this correction. #'Since we only want to ask if any are different than Pom34-GFP, we subset those comparisons, #'then re-calculate adjusted p-values using the number of tests actually done. dunn |> filter(grepl("Pom34", group1), Time != "Post") |> group_by(Experiment, Time) |> mutate(Corr.p.adjust = p.adjust(p, method="holm"), Corr.p.signif = case_when(Corr.p.adjust <= 0.0001 ~ "****", Corr.p.adjust <= 0.001 ~ "***", Corr.p.adjust <= 0.01 ~ "**", Corr.p.adjust <= 0.05 ~ "*", TRUE ~ "ns")) |> select(-p.adj, -p.adj.signif) |> jmv_dataTable() ``` ## CSV files from Python Notebooks These embedded CSVs are the output generated by the Python notebook for peak calling and intensity measurements from line scans. #### Mitosis Line Scan Outputs ```{r} xfun::embed_file("./data/NucRatios_Mlp1-GFP_mitosis.csv") xfun::embed_file("./data/NucRatios_Nup1-GFP_mitosis.csv") xfun::embed_file("./data/NucRatios_Nup2-GFP_mitosis.csv") xfun::embed_file("./data/NucRatios_Pom34-GFP_mitosis.csv") xfun::embed_file("./data/NucRatios_Nup60-GFP_mitosis.csv") ``` #### Meiosis Line Scan Outputs ```{r} xfun::embed_file("./data/NucRatios_Nup2-GFP_meiosis.csv") xfun::embed_file("./data/NucRatios_Pom34-GFP_meiosis.csv") xfun::embed_file("./data/NucRatios_Nup1-GFP_meiosis.csv") xfun::embed_file("./data/NucRatios_Nup60-GFP_meiosis.csv") xfun::embed_file("./data/NucRatios_Mlp1-GFP_meiosis.csv") ```